The rocky road of 55 years of change in the relationship of cardiovascular risk factors to cognition

The mixed evidence that high levels of cardiovascular risk factors (CVRF) are associated with lower cognitive test scores of may be due to confounding of age across studies. We pooled and harmonized individual-level data (30,967 persons, age range 42–96y) from five prospective cohorts to examine the trajectories of betas estimating 1-year-age associations of a cognitive outcome (Digit Symbol Substitution Test; DSST) to five CVRF: systolic and e blood pressure, total cholesterol, fasting glucose and body mass index. Linear and quadratic piecewise regression models were fit to the trajectory patterns of these betas. The trajectories showed with each 1-year age increment, higher CVRF were associated with lower DSST, but associations attenuated toward zero as age increased. In addition, the pattern across age of each CVRF-DSST trajectory ranged from linear to non-liner. Without accounting for participant age in cohort comparisons, conclusions about the potential benefit on cognitive function of modifiable CVRF control will continue to be mixed and lead to delays in developing prevention programs.


Introduction
Late-life cognitive disorders (CD), including cognitive impairment and dementia are devastating. Studies suggest the trajectory to CD begins decades before the onset of clinical symptoms, and that studies of risk factors require a life-course perspective (1). Research over the last two decades has raised the possibility that modi able cardiovascular risk factors (CVRF) may increase the risk for late age CD, and therefore, their control may reduce the occurrence of disease. However, evidence for this hypothesis is mixed.
The inconsistent evidence is quanti ed by the variation in the slopes or risk ratios of the relationship between a CVRF (i.e., blood pressure) and CD outcomes. Reports from observational studies range from an expected higher CVRF level increasing the risk for CD, to a null association, to an unexpected lower CVRF level increasing the risk for CD. (2) The few clinical trials that test the effects on cognitive impairment of intervening on cardiovascular risk factors have largely been negative (3) (4) (5).
Reconciling these mixed ndings is hampered because they are based on single studies of different aged cohorts (i.e. <55; >55, > 65 or >75 yrs. of age); different study designs (i.e. cross-sectional, case-control or longitudinal); different target populations (i.e. clinical volunteers, patient groups, or population-based), different outcome measures (i.e. cognitive testing, clinical adjudication, or algorithmic) and follow-up time; as well as different statistical models to test for associations. Across cohorts, there may also be a different balance of confounding variables, which could bias not only the internal validity of the study, but also the validity of cross-cohort comparisons. It is also a challenge to study dementia prospectively given the long pre-clinical period. (6) Further the risk factor levels and variability may change with age; increasing brain pathology may alter the physiologic activity of a risk factor (leading to 'reverse causation') [1]; or declines in cognition may lead to less vigilant health care (7).
Covariates. Because of their strong association with CVRF and DSST (9), the harmonization models included baseline age, sex, race/ethnicity (White vs. Other), education ( < high school, high school , > high school); and smoking status (never smoked, past smoker, current smoker).

Statistical Methods
Data Harmonization. Using the same method, CVRF and the DSST scores were harmonized to the MESA cohort as it had the widest age interval. The harmonization algorithm is described in detail in Online Resource Text 3 and graphed in Online resource Figures 4. For example, for one risk factor (DBP) and one cohort (ARIC), the harmonized value of DBP was estimated as follows: First, a linear regression model 'DBP= age, sex, education and smoking' (as de ned above) was estimated and the residuals (R ijARIC ) from that model were generated. These residuals re ect how much the individual differed from the cohort model prediction. Next, we ran a linear regression based on MESA data, whereby DBP is predicted by covariates. Then we weighted the ARIC covariates with the covariate betas generated from the MESA model, giving an ARIC/MESA-based prediction of DBP. The residuals from the ARIC ( R ijARIC ) model were then added to the predicted ARIC-MESA DBP giving a harmonized DBP data point for ARIC. According to this method, 'harmonized' CVRF or DSST data points from MESA are identical to the original MESA data points. Comparisons of original and harmonized DSST and CVRF by cohort are illustrated in Online resource 5a-e and by age in Online resource 6).
Statistical models. To estimate the trajectory of the slope (T-slope), data points (i.e. 1-yr slopes) from all harmonized cohorts were combined and each individual timepoint was binned by 1-yr age groups. To optimize sample size per age bin we combined the 1-yr slopes at the lower and upper age range (i.e. age bins ≤ 47 and ≥ 88), giving 42 age bins each with corresponding l CVRF-DSST 1-yr slope estimate.
The overall pattern of CVRF-DSST 1-yr slopes from middle to late age, the trajectory of slopes, T-slope, was calculated by modeling the CVRF-DSST 1-yr-slope (Y) as a linear function of the age in each age bin (X). Upon inspection of graphical data and con rmed by the poor t of the linear model [model 1] it was evident that, with age, some 1-yr slopes began to increase or decrease for some CVRF-DSST slopes. This suggests the association between a CVRF and DSST changes with age, as described above. Based on the patterns of the trajectories of slopes, we sought to de ne the best-tting model. To do this we compared linear, and piecewise linear and quadratic models using the Bayesian information criterion (BIC; MR0468014) (see Online resource Tables 7).
The linear-linear piecewise model estimated each of the two T-slopes (i.e., pieces) as a linear function. The second piecewise model, linear-quadratic, estimated one piece as a linear function, and the second piece as a quadratic function. To decide where to cut the model into two pieces, we estimated agecut, the age at which BIC was the lowest, generally resulting in a strong shift in the direction of the T-slope (see model details in the Supplemental Materials). For all three models, each data point (CVRF-DSST 1-yrslopes per corresponding age bin) was weighted by the inverse of the square of the standard error of the CVRF-DSST 1-yr-slope (See Online Resource Text 8 for details on the model development).
There is some dependency between age bins since some individuals in a 1-yr age bin can also have a timepoint in another older 1-yr age bin which, if not accounted for, would lead to smaller, and thus overly optimistic p-values and con dence limits. To correct for age-bin dependency, we performed 1000 iterations using sampling with replacement (bootstrap) on our initial dataset. For each iteration, we saved the piecewise model parameter estimates to estimate the standard error of those estimates to generate a correct z-score and adjust the p-values and con dence intervals accordingly.
We veri ed the model t by visually comparing the trajectory estimated by the model to a smoothed trajectory based on a procedure with a triangular smoothing window of size 11 (Online Resource Text 9 describes the smoothing algorithms) and that accounts for the variance of the 1-yr-slopes and number of cases per age bin.

Analytical questions
Based on these models we addressed the following questions: Across mid-and late-life: 1. are the 1-yr slopes < 0? (suggesting the 1-yr slopes are negative such that as the level of the CVRF increases, the DSST score decreases) 2. Is the overall trajectory of slopes constant? (i.e., is T-slope = 0, suggesting the association of CVRF to DSST is the same at all ages) 3. If the association of CVRF to DSST is not the same for linear models, or within each of the two pieces of the piecewise models, do the 1-yr-bin slopes increase or decrease? (suggesting the association of the CVRF to DSST changes with age).

Results
The percent women was generally similar across cohorts (range 42.1% to 46.8%). At baseline (Table 1)  The linear-quadratic model for DBP (Fig 1a) had the lowest BIC, with agecut=67. Over the age span, there was considerable variation in the 1-yr slopes. For younger ages (less than 55 y) and older ages (>67 y) 1yr slopes were mostly negative (i.e., < 0 ), suggesting higher DBP is associated with lower DSST scores. Between 55 yo and 70 yo, 1-yr slopes attenuated and became positive close to zero (p = 5.3E-3). Although still positive at age 67, after this, slopes start a downward trend and become increasingly negative until approximately 80 y of age, when again the 1-yr slopes increasingly attenuated to zero (Tslope2sq = 0.00072 (0.00045,0.00099; p = 2.4E-7). This translates into an estimated 0.013 higher DSST score at age 60 y; a 0.21 lower DSST score at age 70 yo; and a 0.27 lower DSST score at age 80 yo for each increment in 1 mmHg DBP, controlling for covariates.
Similar to DBP, the SBP (Figure 1b) linear-quadratic model had the lowest BIC, with agecut=67 y. Individual 1-yr SBP-DSST-slopes were negative across the age span but became increasingly negative as the age, or relative minimum (nadir), of 75.4 yo was reached. This pattern suggests a higher SBP is associated with an even lower DSST score as age increased. After age 75 yo, the 1-yr slopes become increasingly less negative with age. Together these changes re ect a U-shaped relationship from age 67 to 88+ years old [T-slope2sq = 0.00029 (0.00014,0.00044; p = 1.3E-4)]. Based on this model, with each increment in 1 mmHg SBP, adjusting for covariates, there is an estimated 0.021 lower DSST score at age 60 yo; a 0.085 lower DSST at age 70 yo; and at age 80 yo there is a 0.085 lower DSST score.
The BMI trajectory was best modeled (lowest BIC) by the linear-linear model (Figure 1c) analysis, with agecut = 70 yo. For age ≤ 70, for each unit increase in the 1-yr slopes of DSST-BMI, there was an average 0.12 lower DSST; however, the trajectory is not signi cant (p=0.5) and approximately constant. After age 70 yo, 1-yr-slopes become increasingly less negative with age (p=1.5E-3), attenuating to zero and becoming positive slopes around age 80 yo, i.e., a higher BMI was associated with a higher DSST score after 80 years of age. This translates into for each increment of 1 BMI unit, there is an estimated 0.11 lower DSST score at age 60 yo; a 0.10 lower DSST score at age 70 yo; and a 0.009 lower DSST score at age 80 yo.
The best tting model for total cholesterol was the linear-linear model ( Figure 1d) and agecut = 69. Overall, 1-yr slopes were constant and close to zero for age < 69 yo (p = 0.29). After age 69 yo, the magnitude of the 1-yr-slopes of the relationship between DSST and cholesterol tended towards more negative such that a higher cholesterol was associated with a lower DSST score (p=2.2E-3). This translates to an estimated 0.0016 higher DSST score with each increment in 1 mg/dL of cholesterol at age 60 yo; a 0.0029 higher DSST score at age 70 yo; and 0.0056 lower DSST score at age 80 yo.
For fasting glucose, the linear-linear model for fasting glucose (Figure 1e), had the lowest BIC, with agecut = 75 yo. Overall, 1-yr slopes for DSST-fasting glucose associations were negative, meaning that for a unit increase in fasting glucose, there was a lower DSST score. The linear trends before (p=0.07) and after (p=0.057) age 75 were relatively constant but attenuate to zero. For age ≤ 75 yo, the expected 1-yr-slopes were marginally lower by 0.00035 for each increase in 1-unit of fasting glucose. After age 75 yo, the expected 1-yr-slopes was 0.0015 points higher with each 1-unit increment of age, with a drift of the 1-yr slopes attenuating towards zero. This translates into an estimated 0.021 lower DSST score at age 60 yo; a 0.024 lower DSST score at age 70 yo; and a 0.019 lower DSST score at age 80 yo for each increment in 1 unit of fasting glucose level.

Discussion
In 2018, the National Academy of Science summit on prevention of cognitive decline [Preventing Cognitive Decline and Dementia: A Way Forward, National Academies Press, http://www.nap.edu; and https://www.nia.nih.gov/research/administration/recommendations-nih-ad-research-summit-2018] concluded the evidence for control of CVRF to reduce the risk for CD is not robust or consistent enough to support prevention trials or for public health messaging. Therefore to move forward a better understanding of factors underlying the incongruent results.
One source of variability in the evidence may be the study design (cross-sectional vs longitudinal), the age of individual study participants and the mean age of study cohorts. To control for study design we included in our pooled analyses only longitudinal community-based studies that used similar methods to measure cognitive function and key risk factors for cognitive decline. We found, controlling for study design and major confounders of sex, race, education and smoking, the ve CBRF we studied were negatively associated with DSST test scores in both middle-and late-age. However, the magnitude and direction of the CVRF-DSST slope changed with age. All the CVRF 1-year slopes attenuated towards zero as age increased. There were CVRF differences in the age at which the direction of slopes was estimated to change, but generally slopes began to change between the late 60s and mid 70s age-band.
This analysis does not address the speci c reasons for the direction and magnitude of changes in slope trajectories, although other studies can lend insight into contributing factors. Reverse causality has often been proffered as a 'biologic' explanation for nding unexpected (i.e., positive) directionality of associations. Biologically, reverse causality could result from a change in the balance between the brain and periphery regulation of CVRF, whereby the pathology in the brain regulates the risk factor, and not vice versa, as hypothesized. For instance, neurodegenerative pathology in the hypothalamus may affect regulation of glucose, insulin or body weight (25). These changes may result in observations that people on course to develop dementia are losing weight faster and their blood pressure and cholesterol levels are dropping faster than older persons who maintain cognitive function levels (26) (27). However, data are limited on the empirical signi cance these changes have on estimates of the relationships between risk factors and cognition (28).
Although 1-yr CVRF-DSST slopes were negative, several were close to zero, especially at older ages, suggesting other factors may explain null to positive associations between CVRF and DSST. A change in the magnitude or direction of 1-yr slopes may re ect increasing co-morbidity as people age and the proportional reduction in how much any one risk factor can explain CD; the CVRF may lose predictive power with age; or there may be biases due to dropout or mortality [29].
Interestingly, the BP -DSST relationships were the most variable. High blood pressure is a strong risk factor for underlying brain pathology that can lead to CD (30) and possibly, among the ve CVRF we studied, the most direct measure of a factor causing cerebral damage. BP may also be the most sensitive to differences in measurement techniques, timing of measurement and individual/cohort differences such as the prevalence of anti-hypertension use. At the same time, compared to the other risk factors we studied, high BP is likely a stronger risk factor for death at later ages. The more variable results in the DBP compared with SBP analysis is consistent with the epidemiology of blood pressure. With aging, SBP tends to increase, while DBP may decrease due to vascular disease such as increased stiffness (31).
Age is the strongest risk factor for chronic disease, and particularly for CD. Our study suggests comparative analysis of the observational study evidence for CVRF-cognitive associations should be reported and interpreted at least taking into account the age range of the cohort. Speci c studies with a wide age interval should qualitatively or quantitatively examine age interactions. Additional modelling of the reverse causation confound could also contribute to better understanding of how CV risk factors interact with dementing pathophysiology (28, 32) (33).
This study has implications for clinical trials aimed to reduce cognitive decline by treating cardiovascular RF. Observational studies, particularly those based on longitudinal well-characterized cohorts can be pooled and harmonized, and can point to candidate CVRF, and to time intervals when the impact of an intervention is detectable. However the effect sizes are likely small so clinical trials conducted at later ages will need large sample sizes or target vulnerable sub-groups and will need to deliver an intervention that will create large differences between treatment arms, such as SPRINT MIND, where the difference in SBP in the treatment vs control arms was SBP > 14 mmHg (3).
Our analysis has several strengths. We selected cohorts with a similar prospective cohort design and examined core CVRF that have been standardized across studies and time . We also chose one cognitive test that was collected by all studies (34) and is informative in mid and late age. Differences in the distribution of confounding factors were reduced by using common risk factors to adjust the models. Our harmonization algorithm across cohorts successfully standardized measures to one cohort; and by harmonizing the intercepts we aligned the linear slope estimates across cohorts. We also identi ed the 'best tting' linear and non-linear slope trajectory models by comparing Bayesian Information Criteria (BIC) across linear, linear-linear and linear-quadratic equations.
However, several aspects of our study should be noted when interpreting the results. DSST scores were not imputed thus minimizing that source of error. However, because the exam at which a cohort introduced cognitive testing varied, we had to exclude the younger individuals in CARDIA and ARIC. It should also be noted that the trajectories are based on individual 1-yr slopes of CVRF--DSST associations, which are cross-sectional and do not address our question within individual CVRF trajectories. Therefore, while pooling provided a larger sample and a more cohesive picture of differences in CVRF-DSST associations over time, this cross-sectional analysis is subject to the same biases as other cross-sectional studies of 'change by age'-most importantly the bias caused by mortality or selective dropout by age and health condition. We used a single cognitive test with a normal distribution of test scores re ecting psychomotor speed and executive function; tests with different properties may give different results. We did not explore the life-course pattern of CVRF in relation to a dementia outcome, which requires additional assumptions, harmonizing dementia diagnoses and developing prediction models for younger cohorts with no dementia outcomes.
In conclusion, understanding the methodologic and demographic differences among observational studies will aid in the interpretation of CVRF -CD associations and promote more targeted design of clinical trials to prevent cognitive loss in late-life. Association upon request [https://hjarta.is/en/]. The derived data generated in this research will be shared on reasonable request to the corresponding author (launerl@nia.nih.gov).
Author contribution. All authors critically reviewed the manuscript and have approved the manuscript. Lenore J Launer and Osorio Meirelles conceived the project; conducted analyses, interpreted the data and wrote a rst draft; Anthony Arnette collected and created a multi-cohort dataset and analyzed the data; Vilmundur Guðnason provided critical revisions in the text.
Con ict of Interest OM, AA, VG and LJL have no relevant nancial or non-nancial interests to disclose. The authors declare that beyond the listed sources of funding, no other support was received during preparation of this manuscript. Figure 1 1a-1e: Five Cardiovascular risk factor -DSST relationships by 1-yr age bins